The FOR loop is the simplest and fastest method. I do not see a reason to avoid it. But if you really want to:
C = cellfun(@(x) reshape(x, [], 1), C, 'UniformOutput', 0)
% Faster:
C = cellfun(@(x) x(:), C, 'UniformOutput', 0)
A speed comparison:
nC = 1e5; % Create some test data
C = cell(1, nC);
for k = 1:nC
C{k} = zeros(1, 1, randi(20, 1));
end
tic; % The fastes method: a simple loop
C2 = cell(size(C)); % Pre-allocation!!!
for k = 1:numel(C)
C2{k} = C{k}(:);
end
toc
tic;
C3 = cellfun(@(x) x(:), C, 'UniformOutput', 0);
toc
tic;
C4 = cellfun(@(x) reshape(x, [], 1), C, 'UniformOutput', 0);
toc
% Elapsed time is 0.063954 seconds. Loop
% Elapsed time is 0.440766 seconds. cellfun( x(:))
% Elapsed time is 0.530042 seconds. cellfun( reshape(x))
Note, that vectorization is fast for operations, which operate on vectors. CELLFUN is fast for the "backward compatibility mode" to define the operation by a string. Then Matlab performs the operation inside CELLFUN, while for anonymous functions or function handles this function is evaluated. But this happens in a FOR loop also, so prefer the direct approach.
